A Hybrid Intelligent Model for Software Cost Estimation
This addresses the challenge of software cost estimation for software engineers, but it is incremental as it combines existing neural network and fuzzy models.
The paper tackled the problem of inaccurate software development effort estimation by proposing a hybrid neuro-fuzzy model, which improved estimation accuracy by 18% based on the Mean Magnitude of Relative Error criterion.
Accurate software development effort estimation is critical to the success of software projects. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics. In this paper, a hybrid in-telligent model combining a neural network model integrated with fuzzy model (neuro-fuzzy model) has been used to improve the accuracy of estimating software cost. The performance of the proposed model is assessed by designing and conducting evaluation with published project and industrial data. Results have shown that the proposed model demonstrates the ability of improving the estimation accuracy by 18% based on the Mean Magnitude of Relative Error (MMRE) criterion.